Obtaining EVLA Data: 3C 391 Example: Difference between revisions

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== Appendix: Obtaining Data: 3C 391 Example ==
#REDIRECT [[Obtaining Data: 3C 391 Example]]
 
For the purposes of the summer school tutorials, a small number of initial processing steps had been applied.  Here we describe in more detail the series of steps that one is likely to have to conduct to obtain a data set similar to what was used for the summer school tutorials, using the 3C 391 data set as an example.
 
The original test data were taken on 2010 April 24, and are stored as file TDEM0001_sb1218006_1.55310.33439732639 .
 
=== Acquiring data from the Archive ===
 
=== Converting to a measurement set ===
 
<source lang="python">
importevla(asdm='TDEM0001_sb1218006_1.55310.33439732639',vis='3c391_mosaic_fullres.ms',flagzero=True,shadow=True)
</source>
 
=== Initial flagging ===
 
 
Depending upon the science goals and the details of the observation, averaging in time, frequency, or both may be possible at this stage.  For instance, the 3C 391 data used in the summer school tutorial were acquired with a 1-second sampling in the D configuration.  Given the size of 3C 391 itself, and the fact that there are no other strong nearby sources, it makes sense to average these data in time (and possibly in frequency as well, although that was not done for the summer school).  For the summer school tutorial itself, we also restricted ourselves to just a single spectral window, even though the observations were acquired with two spectral windows.  Thus,
 
<source lang="python">
split(vis='3c391_ctm_mosaic.ms',outputvis='3c391_ctm_mosaic_10s.ms',datacolumn='data',
      spw='0',width=1,timebin='10s')
</source>
results in a single spectral window, with an unchanged frequency resolution, averaged to 10-second sampling.

Latest revision as of 17:01, 8 May 2016